A Lightweight Multi-View Approach to Short-Term Load Forecasting
This work addresses the need for efficient and stable forecasting in energy domains, though it is incremental as it builds on existing methods to reduce complexity.
The paper tackles the problem of overfitting and instability in short-term load forecasting by proposing a lightweight multi-view approach with single-value embeddings and scaled time-range input, achieving competitive performance with significantly fewer parameters and robustness across noisy or sparse datasets.
Time series forecasting is a critical task across domains such as energy, finance, and meteorology, where accurate predictions enable informed decision-making. While transformer-based and large-parameter models have recently achieved state-of-the-art results, their complexity can lead to overfitting and unstable forecasts, especially when older data points become less relevant. In this paper, we propose a lightweight multi-view approach to short-term load forecasting that leverages single-value embeddings and a scaled time-range input to capture temporally relevant features efficiently. We introduce an embedding dropout mechanism to prevent over-reliance on specific features and enhance interpretability. Our method achieves competitive performance with significantly fewer parameters, demonstrating robustness across multiple datasets, including scenarios with noisy or sparse data, and provides insights into the contributions of individual features to the forecast.